code: def _get_var_with_second_order(pdA2, f0, pdA):
      """Auxiliary function to compute d(var(A))/dp = d<A^2>/dp -2 * <A> *
      d<A>/dp for the variances.
  
      The result is converted to an array-like object as some terms (specifically f0) may not be array-like in every case.
  
      Args:
          pdA (tensor_like[float]): The analytic derivative of <A>.
          pdA2 (tensor_like[float]): The analytic derivatives of the <A^2> observables.
          f0 (tensor_like[float]): Output of the evaluated input tape. If provided,
              and the gradient recipe contains an unshifted term, this value is used,
              saving a quantum evaluation.
      """
      # Only necessary for numpy array with shape () not to be float
      if any(isinstance(term, np.ndarray) for term in [pdA2, f0, pdA]):
          # It breaks differentiability for Torch
          return qml.math.array(pdA2 - 2 * f0 * pdA)
      return pdA2 - 2 * f0 * pdA
  
imports: from typing import Sequence, Callable
  from functools import partial
  import numpy
  import pennylane
  from pennylane.measurements import VarianceMP
  from pennylane import transform
  from pennylane.transforms.tape_expand import expand_invalid_trainable
  from pennylane.gradients.gradient_transform import _contract_qjac_with_cjac
  from .finite_difference import finite_diff
  from .general_shift_rules import _iterate_shift_rule, frequencies_to_period, generate_shifted_tapes, process_shifts
  from .gradient_transform import _all_zero_grad, assert_no_state_returns, assert_no_tape_batching, assert_multimeasure_not_broadcasted, choose_trainable_params, find_and_validate_gradient_methods, _no_trainable_grad, reorder_grads
label_desc: Use image node_id 11 for calling a global function with example usage: _get_var_with_second_order(pdA2, f0, pdA) and returns: unknown, qml